System and method for analyzing and validating oil and gas well production data

ABSTRACT

A system and method for analyzing and validating oil and gas well production data is disclosed. The system includes a network, a server connected to the network, and a set of wells connected to the network. In a preferred embodiment, the server is programmed to store and execute the method. The method includes the steps of collecting a set of data from the set of wells, performing an first RPI® evaluation on the set of data, creating a matched data set from the set of data, segregating the matched data set into a set of comparison groups, normalizing each comparison group of the set of comparison groups, calculating a set of performance metrics between a subset of the set of comparison groups, and calculating a probability for each performance metric of the set of performance metrics.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application No.61/901,948, filed Nov. 8, 2013. The patent application identified aboveis incorporated here by reference in its entirety to provide continuityof disclosure.

FIELD OF THE INVENTION

The field of this application is oil and gas well management, namely,providing a system and method for evaluating the production,characteristics and economic value of oil and gas wells.

BACKGROUND OF THE INVENTION

Oil and gas reservoirs are underground formations of rock containing oiland/or gas. The type and properties of the rock vary by reservoir andwithin reservoirs. For example, a porosity and a permeability ofreservoir rock may vary from well to well within a reservoir. Theporosity is the percentage of pore volume, or void space, within thereservoir rock that can contain fluids. The permeability is an estimateof the ability of reservoir rock to permit the flow of fluids.

Many oil and gas wells in the United States produce fromlow-permeability, shale, or “tight” reservoirs. These reservoirs presentmany challenges in drilling, completions, and reservoir evaluation. Inorder to produce at economic rates, low-permeability wells must becompleted by a stimulation treatment, such as hydraulic fracturing. Atypical fracture treatment represents a significant fraction of thetotal cost of drilling and completing the well. Hence, whether or not afracture treatment will be economically productive is a question ofgreat interest to the operator.

In conventional reservoirs, determining the success of a stimulationtreatment is performed by conducting and analyzing a buildup test orother type of pressure transient test after the treatment is applied tothe reservoir. The rate at which a pressure transient moves through areservoir is a function of the permeability. As a result,low-permeability reservoirs require long test times to sample asignificant portion of the reservoir. Further, it is now known that theshut-in associated with the build-up test can significantly harm awell's productivity. Therefore, pressure transient tests are of limitedapplication for hydraulically fractured wells in low-permeabilityreservoirs, where weeks or years are required to obtain usable pressuredata and thereby increasing operation costs.

In an attempt to reduce costs, production of a well in the reservoir maybe estimated prior to the proposed stimulation treatment or in order toselect the best possible new well locations to maximize profitability.Production of oil, gas, and/or byproducts thereof from a well is usuallyestimated by analyzing production data. Because direct measurement offuture production data is not possible in forecasting overallproduction, the production estimations are frequently unreliable.

The prior art has attempted to solve the problem of unreliable estimateswith limited success. For example, U.S. Pat. No. 7,225,078 to Shelley etal. discloses a system and method for predicting production of a well.The system collects and processes data from a set of wells of areservoir to generate a production prediction model for the set ofwells. The data collected are logs from the set of wells, including MRIlogs. The system clusters the log data from various wells based onsimilar predetermined characteristics, preferably by the MRI log, togenerate a set of log profiles. The set of log profiles are correlatedwith validation indicators. The system optimizes the log profiles byreducing or adding the number of clusters in a log profile to obtain orapproach a linear alignment of the log profile with the validationindicators. A set of production indicators is associated with each logprofile. The set of production indicators may be based on average swabtest results or a subset of the validation indicators. The optimized setof log profiles and associated production indicators is stored as aproduction prediction model. However, the system and method in Shelleycannot evaluate the reliability of the prediction model.

U.S. Pat. No. 7,369,979 to Spivey discloses a method for forecastingperformance of wells in multilayer reservoirs having commingledproduction. A multi-layer predictive model is first calculated includinga fluid property model, a tubing pressure gradient model, and a singlelayer predictive model. A non-linear regression module is used togenerate synthetic models to compare to observed data. The method beginsby collecting data a well in a reservoir. The fluid property model iscalculated from the data over a predetermined time. The tubing pressuregradient model is calculated from the fluid property model. A singlelayer prediction model is calculated for each layer in the reservoir,thereby generating the multi-layer predictive model. A syntheticproduction history and a set of synthetic production log data aregenerated using the multi-layer prediction model and the non-linearregression module to compare with an observed production history and anobserved production log data, respectively. However, like Shelley, theprediction model in Spivey does not include any means for calculatingthe accuracy of the prediction model.

The prior art does not disclose or suggest a system and method foranalyzing and validating oil and gas well production data. Therefore,there is a need in the art for a system and method for predicting theproduction of oil and gas wells and verifying the accuracy of theprediction. Especially for shale wells, there is a need for theinterpretability of water data.

SUMMARY

A system and method for analyzing and validating oil and gas wellproduction data is disclosed. The system includes a network, a serverconnected to the network, and a set of wells connected to the network.In a preferred embodiment, the server is programmed to store and executethe method. The method includes the steps of collecting a set of datafrom the set of wells, performing an RPI® evaluation on the set of data,creating a matched data set from the set of data, segregating thematched data set into a set of comparison groups, normalizing eachcomparison group of the set of comparison groups, calculating a set ofperformance metrics between a subset of the set of comparison groups,and calculating a probability for each performance metric of the set ofperformance metrics.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosed embodiments will be described with reference to theaccompanying drawings.

FIG. 1 is a schematic for a system for analyzing oil and gas wellproduction data of a preferred embodiment.

FIG. 2 is a flowchart of a production validation process of a preferredembodiment.

FIG. 3A is a flowchart of an analysis process of a preferred embodiment.

FIG. 3B is a set of graphs of a production signature verificationprocess of a preferred embodiment.

FIG. 3C is a set of graphs of a Miller-Dyes-Hutchinson (“MDH”) semi-logtype curve history matching process of a preferred embodiment.

FIG. 3D is a set of graphs of a Pseudo-Steady State (“PSS”) type curvehistory matching process of a preferred embodiment.

FIG. 3E is a set of graphs depicting a set of history matched curves.

FIG. 4 is a flowchart of a performance metric evaluation process of apreferred embodiment.

DETAILED DESCRIPTION

It will be appreciated by those skilled in the art that aspects of thepresent disclosure may be illustrated and described herein in any of anumber of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Therefore, aspects of the present disclosuremay be implemented entirely in hardware, entirely in software (includingfirmware, resident software, micro-code, etc.) or combining software andhardware implementation that may all generally be referred to herein asa “circuit,” “module,” “component,” or “system.” Further, aspects of thepresent disclosure may take the form of a computer program embodied inone or more computer readable media having computer readable programcode embodied thereon.

Any combination of one or more computer readable media may be utilized.The computer readable media may be a computer readable signal medium ora computer readable storage medium. For example, a computer readablestorage medium may be, but not limited to, an electronic, magnetic,optical, electromagnetic, or semiconductor system, apparatus, or device,or any suitable combination of the foregoing. More specific examples ofthe computer readable storage medium would include, but are not limitedto: a hard disk, a random access memory (“RAM”), a read-only memory(“ROM”), an erasable programmable read-only memory (“EPROM” or Flashmemory), an appropriate optical fiber with a repeater, a portablecompact disc read-only memory (“CD-ROM”), an optical storage device, amagnetic storage device, or any suitable combination of the foregoing.Thus, a computer readable storage medium may be any tangible medium thatcan contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. The propagated data signal maytake any of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable signal medium may be transmitted usingany appropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, or any suitable combination thereof.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, C++, C#, .NET, Objective C, Ruby, Python SQL, VisualBasic, or other modern and commercially available programming languages.

Referring to FIG. 1, system 100 includes server 101 connected to network102, oil wells 103, 104, and 105, each of which is connected to network102, and gas wells 106, 107, and 108, each of which is connected tonetwork 102.

Server 101 includes processor 109 and memory 110 connected to processor109. Production validation process 111 is stored in memory 110 and isexecuted by processor 109. Production validation process 111 collectsand processes data from each of oil wells 103, 104, and 105 and gaswells 106, 107, and 108, as will be further described below.

In a preferred embodiment, each of oil wells 103, 104, and 105 and gaswells 106, 107, and 108 includes a set of sensors that receive andtransmit data to server 101 through network 102. Any sensor known in theart may be employed.

In another embodiment, data from each of oil wells 103, 104, and 105 andgas wells 106, 107, and 108 is manually entered into memory 110 ofserver 101.

Referring to FIG. 2, production validation process 200 will be furtherdescribed. In step 201, a data set from each of a set of oil wells andgas wells are collected. In a preferred embodiment, the set of oil andgas wells includes a baseline well group and a validation well group,from which a baseline well group set of data and a validation well groupset of data is respectively collected. In this embodiment, the baselinewell group is a group of wells to which no operating or completionpractice will be applied, i.e., a control group of wells. In thisembodiment, the validation group is a group of wells that an operatingor completion practice will be applied. Any operating or completionpractice known in the art may be employed. In this embodiment, each ofthe baseline well group and the validation well group data sets includesa completion and workover history data set and a production history dataset as shown in Table 1 below.

TABLE 1 Data Sets for Baseline Well Group and Validation Well GroupCompletion and Workover History Production History Wellbore diagramInitial flowback report Initial stimulations Daily production historyWorkover Any pumper's notes

In this embodiment, the completion and workover history data setincludes a wellbore diagram, an initial stimulations data set, and aworkover data set. The wellbore diagram includes a set of stagelocations, a set of measured depths, an average true vertical depth(“TVD”), and an initial tubulars description and length. The initialstimulations data set is by stage as pumped, not as designed or asproposed stimulations. The initial stimulations data set includes: aworking fluid volume that is used to place packer/ball, fire guns, cleanout, and any working fluid for any other purpose; a pad fluid volume; atreating fluid volume, a type of treating fluid, and compositions; aproppant type and placement schedule; a flush volume; and a treatingpressure history. The workover data set includes data from anyartificial lift installation, or operation, and any other in-welloperations, including hot oiling, and paraffin scraping. The workoverdata set includes a kill fluid type and volume, and any workover report.

In this embodiment, the production history data set includes an initialflowback report, a daily production history, and any pumper's notes. Theinitial flowback report is preferably measured at least hourly. Othermeasuring frequencies may be employed.

In a preferred embodiment, the initial flowback report includes themeasured rates of produced phases of each well, the surface wellheadflowing pressure, the surface wellhead flowing temperature, any workingfluid volumes for a gas lift, jet pump, or any other means, and anyworking fluid injection pressures.

In step 202, the data from the wells is analyzed, as will be furtherdescribed below. Other data known in the art may be employed.

In step 203, a performance probability for each metric of the analyzeddata is calculated. In a preferred embodiment, the analysis of variance(“ANOVA”) is used to calculate the performance probability for eachmetric. Other methods of calculating probability known in the art may beemployed.

In step 204, the performance probability is reported. In one embodiment,a report document is created. In this embodiment, the report documentincludes a set of histograms, a set of comparative performance graphs,and a set of difference values, as will be further described below. Inanother embodiment, a visual presentation of the performance probabilityis created. Any reporting means and mediums known in the art may beemployed.

In one embodiment, each metric and performance probability is reportedin a table, such as shown in Table 2 below. In this example below, agroup of wells using an organic nano-fluid interfacial tension modifier(“OnF”), i.e., the validation group, is compared to a group of wells notusing the “OnF”, i.e., the baseline group.

TABLE 2 Probability that “OnF” Wells are “OnF” Non-“OnF different fromWells Wells Non-“OnF” Performance Metric Averages Wells Well count 10 6Total Proppant Pumped (K lbs) 908 940 26.1 Reservoir Conductivity (mDs*ft) 1.72 1.40 89.8 Initial Effective Fracture 177 112 97.4 Length (ft)Effective Fracture Length 13.9 41.4 94.6 Change due to Damage (ft)Estimated Gas Recovery 9787 7300 99.3 (20 yrs @200 psig) (MMCF) “NetPresent Volume” 3187 2319 99.5 (20 yr estimated gas recovery @ 20%discount) (MMCF)

Referring to FIG. 3A, step 202 will be further described as analysisprocess 300. In step 301, the production history data set of each wellis prepared. In this step, the individual well production history datasets are merged into a Microsoft Excel spreadsheet with an Excel pagefor each well data set.

In step 302, the spreadsheet pages are imported into RPI® ProductionAnalysis software available from Performance Sciences, Inc. (“RPI®”). Inthis step, a TVD, an estimated initial pressure, a reservoirtemperature, an estimated porosity, an estimated contributing thickness,a set of tubulars descriptions, and workover events including refracsand artificial lift installation are imported into RPI®.

In step 303, a first RPI® evaluation is performed using RPI®. In thisstep, whether the individual well data sets contain usable productionsignatures is verified. As used in this application, a productionsignature is a qualitative graphically shaped pattern exhibited by theproduction data when plotted which is diagnostic of the reservoir systemcondition. For example, a transient inducing event is a productionsignature.

In this step, transient inducing events are identified. Referring toFIG. 3B, screen 309 includes menu 310 and windows 311, 312, 313, and 314that include graphs 315, 316, 317, and 318, respectively. Transientinducing events are identified in graph 318 using transient tool 319 andinserted into the data set for each well by selecting “calc” tool 322.For example, portion 320 of curve 321 indicates a transient inducingevent has occurred. Transient tool 319 is selected and portion 320 ishighlighted by “clicking” and “dragging” the cursor over portion 320.Once portion 320 is highlighted, portion 320 is inserted into the dataset by selecting “calc” tool 322. The resulting signatures are examinedfor evidence of depletion or interference from offset wells,frac-through, and natural fracture overprint.

In a preferred embodiment, graph 315 is an Agarwal-Gringarten Log-LogType Curve (“AGTC”) graph. In this embodiment, graph 316 is aMiller-Dyes-Hutchinson (“MDH”) semi-log graph. In this embodiment, graph317 is a Pseudo-Steady State (“PSS”) graph. In this embodiment, graph318 is an Arps production decline graph.

Returning to FIG. 3A in step 304, the data set used for performancematching is determined. In this step, whether a full data set or alimited or partial data set for a predetermined timeframe is determinedas the performance matching data set. In this step, incomplete well datasets may be excluded.

In step 305, a second RPI® evaluation using RPI® is performed on thematching data set determined in step 304. In this step, the matchingdata set of each well is matched using the history-matching function inRPI® based on its production history for each of a reservoir quality orconductivity, an effective reservoir/wellbore connectivity, apressure-contacted volume, and an initial reservoir average pressure toa predicted production data set. The predicted production data set and acorresponding predicted production curve are generated by RPI®, as willbe further described below. The reservoir quality is described by areservoir conductivity measured in terms of the product of thepermeability times thickness measured from the matching process, as willbe further described below. The effective reservoir/wellboreconnectivity can be described by an apparent fracture half-length orenhanced conductivity reservoir volume. The pressure contacted volume isthe volume providing pressure support for the well's production.

By history-matching the data curves for each well, the predictedproduction data curve will be aligned with production history data curvefor each well, thereby producing predicted production data that followsa pattern produced by the production history data. Once the data curvesare aligned, the RPI® software recalculates each predicted productiondata metric to closer create a matched data set. Each metric of thematched data set follows each historical production data metric, as willbe further described below.

Referring to FIG. 3C, AGTC graph 330 includes curve 331 andinfinite-acting curve 332. Production decline graph 334 has predictedproduction curve 335 and historical data curve 336. MDH graph 323includes historical data curve 324 and predicted curve 325. Match bar326 of MDH graph 323 is selected and moved to align with historical datacurve 324. Handles 327 and 328 may be selected and dragged to adjust theslope of match bar 326 to further align match bar 326 with historicaldata curve 324. Adjusting the slope of match bar 326 will change thevalue of predicted permeability 329 and apparent fracture half-length333. Aligning match bar 326 with historical data curve 324 will alignpredicted curve 325 with historical data curve 324. When predicted curve325 is aligned with historical data curve 324, predicted permeability329 and apparent fracture half-length 333 is recalculated for the dataset.

Referring to FIG. 3D, predicted permeability 329 and apparent fracturehalf-length 333 have been recalculated. Curve 331 is aligned withinfinite-acting curve 332 and predicted production curve 335 is closerin alignment to historical data curve 336 than that shown in FIG. 3C.

PSS graph 337 has historical data curve 338 and predicted curve 339.Historical data curve 338 is the square root of curved portion 345 ofhistorical data curve 324 of MDH graph 323. Predicted curve 339 is thesquare root of curved portion 346 of predicted curve 325 of MDH graph323. Predicted curve 339 will be aligned with historical data curve 338using match bar 340 as previously described. Match bar 340 has handles341 and 342. Changing the slope of match bar 340 recalculates the valuesof drained area acres 343 and drained area transient time 344.

Referring to FIG. 3E, drained area acres 343 and drained area transienttime 344 have been recalculated. Historical data curve 338 is alignedwith predicted curve 339 of PSS graph 337 and historical data curve 324of MDH graph 323 is further aligned with predicted curve 325 of MDHgraph 323. Predicted production curve 335 is now aligned with historicaldata curve 336. The predicted production data set is recalculated tocreated a matched data set. The matched data set follows the curvedpattern produced by the historical production data set and is displayedin menu 310.

In this step, a set of log cumulative frequency histograms of thematched data set is created and examined for any data outliers. In apreferred embodiment, a data outlier is defined as data beyond at leastone logarithmic standard deviation. Other definitions for a data outliermay be used.

In this step, the matched data set is reviewed for any data outliers.The matched data can be modified as needed, recognizing that alterationof the observed hydrocarbon production data implies a fiduciaryobligation, since it is the basis for financial transactions.

In this step, any impact of shut-in events, changes inreservoir/wellbore connectivity, or reservoir conductivity areevaluated.

Returning to FIG. 3A in step 306, a third RPI® evaluation is performedto ensure consistency of the results of steps 303 and 305 by repeatingsteps 303 and 305.

In step 307, a set of production sums for each well is created from thematched data set based on the shortest reported production history ofthe set of wells. The set of production sums include an average pressuredrawdown history for each sum. In this step, any working fluid volumesmay be used to correct the reported volumes of that working fluid phase.In this step, each production sum is converted to a cumulative oil orgas equivalent as needed, for example, a BTU based equivalent or avalue-based equivalent.

In step 308, a set of performance metrics is evaluated for the matcheddata set, as will be further described below.

Referring to FIG. 4, step 308 will be further described as process 400.In step 401, the matched data set is stratified or segregated intocomparison groups. For example, the matched data set may be segregatedinto a first group that employs an organic nano-fluid interfacialtension modifier (“OnF”), i.e., the validation group, and a second groupthat does not employ the OnF, i.e., the baseline group. Other comparisongroups in any number may be employed.

In step 402, a set of histograms is created for each comparison group.In a preferred embodiment, each set of histograms is a set of raw,un-normalized log cumulative frequency histograms. In this embodiment,each histogram includes a reservoir quality, a reservoir/wellboreconnectivity, a treatment size, and a set of un-normed production sumsfor each well.

In step 403, each histogram is examined for any data outliers. In thisstep, a potential cause for any data outlier is assessed and a responseaction is determined that includes whether to include the data outlier,weight the data outlier, or exclude the data outlier.

In a preferred embodiment, a data outlier is defined as data beyond atleast one logarithmic standard deviation. Other definitions for a dataoutlier may be used.

Any method of weighing a data outlier known in the art may be employed.

In step 404, an initial cumulative equivalent production normalizationis performed for each comparison group for the reservoir quality, thepressure drawdown, the treatment size, a delay from end of stimulationto first production, a shut-in impact, and an effect of any artificiallift. In this step, the normalization process, based on the physicalprinciples of the flow of fluids in a porous media, “ratios” the metricbeing normalized according to whether the normalizing parameter, such asreservoir quality, is larger or smaller than the population log-normalmean or other proper statistical moment.

In step 405, the reservoir/wellbore connectivity is normalized for atreatment size, a proppant type, and a proppant conductivity for eachcomparison group. In a preferred embodiment, the treatment size is theamount of placed proppant in pounds. As needed, the treatment size mayrequire further normalization for proppant conductivity adjustedaccording to published tables, as updated from time to time, for theambient stress, temperature and age of the proppant.

In step 406, any remaining performance metrics may be examined andidentified for calculation as described below, as needed. For example,any treating pressures, long-term performance behavior, response toartificial lift, and resistance to damage mechanisms, includingsubsequent workovers, shut-ins, and frac-throughs, may be examined.

In step 407, a potential performance value difference between comparisongroups is calculated for each metric, including the set of productionsums. In this step, a forecast value difference for different events iscalculated as needed. For example, a forecast value difference may becalculated for any stimulation, delay to first production, shut-ins,artificial lift installation, and re-stimulation.

It will be appreciated by those skilled in the art that modificationscan be made to the embodiments disclosed and remain within the inventiveconcept. Therefore, this invention is not limited to the specificembodiments disclosed, but is intended to cover changes within the scopeand spirit of the claims.

1. In a system comprising a network, a server connected to the network,a set of wells connected to the network, the server programmed to storeand execute instructions that cause the system to perform a methodcomprising the steps of: collecting a set of data from the set of wells;creating a matched data set from the set of data; segregating thematched data set into a set of comparison groups; normalizing eachcomparison group of the set of comparison groups; calculating a set ofperformance metrics between a subset of the set of comparison groups;and, calculating a probability for each performance metric of the set ofperformance metrics.
 2. The method of claim 1, further comprising thestep of verifying a set of production signatures for the set of data. 3.The method of claim 1, wherein the set of data further comprises a setof historical production data for each well of the set of wells, andwherein the step of creating a matched data set further comprises thesteps of: generating a historical production curve from the set ofhistorical production data for each well of the set of wells; generatinga set of predicted production data from the historical production datafor each well of the set of wells; generating a predicted productioncurve from the set of predicted production data for each well of the setof wells; matching the predicted production curve with the historicalproduction curve for each well of the set of wells; and, recalculatingthe set of predicted production data.
 4. The method of claim 3, furthercomprising the step of calculating a set of production sums from thematched data set for each well of the set of wells.
 5. In a systemcomprising a network, a server connected to the network, a set of wellsconnected to the network, the server programmed to store and executeinstructions that cause the system to perform a method comprising thesteps of: collecting a set of data from the set of wells; creating amatched data set from the set of data; generating a set of performancemetrics from the matched data set; and, calculating a probability foreach performance metric of the set of performance metrics.
 6. The methodof claim 5, wherein the step of creating a matched data set from the setof data further comprises the step of verifying a set of productionsignatures from the set of data.
 7. The method of claim 5, wherein theset of data further comprises a set of historical production data foreach well of the set of wells, and wherein the step of creating amatched data set further comprises the steps of: generating a historicalproduction curve from the set of historical production data for eachwell of the set of wells; generating a set of predicted production datafrom the historical production data for each well of the set of wells;generating a predicted production curve from the set of predictedproduction data for each well of the set of wells; and, matching thepredicted production curve with the historical production curve for eachwell of the set of wells.
 8. The method of claim 7, further comprisingthe step of recalculating the set of predicted production data.
 9. Themethod of claim 5, wherein the step of generating a set of performancemetrics from the matched data set further comprises the step ofperforming a metric evaluation on the matched data set for the set ofwells.
 10. The method of claim 9, wherein the step of performing ametric evaluation on the matched data set for the set of wells furthercomprises the steps of: segregating the matched data set into a set ofcomparison groups; and, normalizing each comparison group of the set ofcomparison groups.
 11. The method of claim 10, further comprising thestep of calculating a set of production sums from the matched data setfor each well of the set of wells in each comparison group of the set ofcomparison groups.
 12. The method of claim 11, wherein the step ofgenerating a set of performance metrics from the set of data furthercomprises the step of calculating a set of difference values between asubset of the set of comparison groups.
 13. A system for verifying oiland gas production, comprising: a network; a server, connected to thenetwork; a set of wells connected to the network; the server programmedto carry out the steps of: receiving a set of data from the set ofwells; creating a matched data set from the set of data; generating aset of performance metrics from the matched data set; and, calculating aprobability for each performance metric of the set of performancemetrics.
 14. The system of claim 13, wherein the server is furtherprogrammed to carry out the step of verifying a set of productionsignatures from the set of data.
 15. The system of claim 13, wherein theset of data further comprises a set of historical production data foreach well of the set of wells, and wherein the server is furtherprogrammed to carry out the steps of: generating a historical productioncurve from the set of historical production data for each well of theset of wells; generating a set of predicted production data from thehistorical production data for each well of the set of wells; generatinga predicted production curve from the set of predicted production datafor each well of the set of wells; and, matching the predictedproduction curve with the historical production curve for each well ofthe set of wells.
 16. The system of claim 15, wherein the server isfurther programmed to carry out the step of recalculating the set ofpredicted production data.
 17. The system of claim 13, wherein theserver is further programmed to carry out the step of performing ametric evaluation on the matched data set for the set of wells.
 18. Thesystem of claim 17, wherein the server is further programmed to carryout the steps of: segregating the matched data set into a set ofcomparison groups; and, normalizing each comparison group of the set ofcomparison groups.
 19. The system of claim 18, wherein the server isfurther programmed to carry out the step of calculating a set ofproduction sums from the matched data set for each well of the set ofwells in each comparison group of the set of comparison groups.
 20. Thesystem of claim 19, wherein the server is further programmed to carryout the step of calculating a set of difference values between a subsetof the set of comparison groups.